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Laplacian Likelihood-Based Generalized Additive Model for RNA-Seq Analysis of Oral Squamous Cell Carcinoma

Laplacian Likelihood-Based Generalized Additive Model for RNA-Seq Analysis of Oral Squamous Cell Carcinoma

Vinai George Biju, Prashanth C. M.
Copyright: © 2021 |Volume: 15 |Issue: 4 |Pages: 19
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859857|DOI: 10.4018/IJCINI.20211001.oa18
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MLA

Biju, Vinai George, and Prashanth C. M. "Laplacian Likelihood-Based Generalized Additive Model for RNA-Seq Analysis of Oral Squamous Cell Carcinoma." IJCINI vol.15, no.4 2021: pp.1-19. http://doi.org/10.4018/IJCINI.20211001.oa18

APA

Biju, V. G. & C. M., P. (2021). Laplacian Likelihood-Based Generalized Additive Model for RNA-Seq Analysis of Oral Squamous Cell Carcinoma. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(4), 1-19. http://doi.org/10.4018/IJCINI.20211001.oa18

Chicago

Biju, Vinai George, and Prashanth C. M. "Laplacian Likelihood-Based Generalized Additive Model for RNA-Seq Analysis of Oral Squamous Cell Carcinoma," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.4: 1-19. http://doi.org/10.4018/IJCINI.20211001.oa18

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Abstract

The study's objective is to identify the non-linear relationship of differentially expressed genes that vary in terms of the tumour and normal tissue and correct for any variations among the RNA-Seq experiment focused on Oral squamous cell carcinoma samples from patients. A Laplacian Likelihood version of the Generalized Additive Model is proposed and compared with the regular GAM models in terms of the non-linear fitting. The Non-Linear machine learning approach of Laplacian Likelihood-based GAM could complement RNA-Seq Analysis mainly to interpret, validate, and prioritize the patient samples data of differentially expressed genes. The analysis eases the standard parametric presumption and helps discover complexity in the association between the dependent and the independent variable and parameter smoothing that might otherwise be neglected. Concurvity, standard error, deviance, and other statistical verification have been carried out to confirm Laplacian Likelihood-based GAM efficiency.